1. Field of the Invention
The present invention relates to a system and method for an Electronic Medical Records system which utilizes a learning function associated with a care provider's preferences and tendencies in combination with a Knowledge Base.
2. Description of the Related Art
The present invention relates to electronic medical records (“EMRs”) which are computerized medical records created in an organization that delivers care such as a doctor's office. The EMRs are typically part of a health information system that allows for the storage, retrieval, and manipulation of the medical records. Typically, EMRs are accessible from a workstation or in some cases through a portable or mobile device which utilizes a standard graphical user interface to retrieve, view, and update or edit the medical records.
EMRs are utilized to help manage a heavy patient load but also to assist the provider to meet the proper standards and documentation required by insurance companies before payment. The EMR systems typically require recordation of pertinent facts and findings of an individual's health and medical history including exams, tests, and treatments. A typical patient encounter includes a history taking segment, a physical exam, an analysis and diagnosis, a treatment phase, a medical record documentation phase, and a communication phase which might include instructions to the patient or a report to a referring doctor.
Currently, some EMR systems are able to monitor events and analyze patient data to predict, detect, and perhaps prevent adverse events. Such events might include pharmacy orders, lab results, and other data from services provided and from the provider's notes. However, most of these systems require significant human intervention which hinders the provider's speed of attending to patients.
Further, current EMR systems fail to provide a health information system or an electronic medical records system which automates the generation of the provider's note and learns the provider's preferences and tendencies in diagnosis and order selection as well as plans for treating patients. Current systems require the provider to either dictate notes for transcription, write down notes or type them into an EMR system all of which increase the time to process a patient diverting critical patient to doctor (or care provider) time.
Therefore, what is needed is a system which learns the doctor's preferences and tendencies in diagnosing and treating patients while automating the generation of the provider's notes, work orders, and communication.